ArticleBMC oral health2024
Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Applications of AI-based deep learning models for detecting dental caries on intraoral images - a systematic review.Evidence-based dentistry · 2025Pooled it
- DentSeg3: A Three-Branch Specialized Fusion Network for Dental Lesion Segmentation in Color Images.Journal of imaging informatics in medicine · 2026Article
- Automated classification of maxillary and mandibular dental views on intraoral photographs: a comparative benchmark study and mobile proof-of-concept.BMC oral health · 2026Article
- Remote assessment of caries, MIH, and plaque on intraoral 3D scan images: Findings from the LIFE Child study.Clinical oral investigations · 2026Article
- Mobile Imaging-Based Machine Learning for Dental Caries, Sealants, and Fluorosis: Protocol for a Cross-Sectional Model Development and Validation Study.JMIR research protocols · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Artificial intelligence and oral photography: an approach to the epidemiology of dental caries.Revista de saude publica · 2026Article
- Automatic recognition of oral health status in mixed dentition via intraoral photography.BMC oral health · 2025Article
- A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.BMC oral health · 2025Article
- Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation.BMC oral health · 2024Article
- Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs.Journal of clinical medicine · 2024Article
- Review
- Utilizing Deep Learning for Diagnosing Radicular Cysts.Diagnostics (Basel, Switzerland) · 2024Article
- Diagnostic efficiency of digital photography and AI-assisted image interpretation in dental caries examination: An umbrella review.Journal of oral biology and craniofacial researchArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos.
methodsA total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test.
resultsAt the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880-0.958) for caries detection and 0.902 (95% CI, 0.853-0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection.
conclusionsThe proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.
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